Overview

Brought to you by YData

Dataset statistics

Number of variables20
Number of observations2290424
Missing cells3494900
Missing cells (%)7.6%
Duplicate rows62785
Duplicate rows (%)2.7%
Total size in memory1.8 GiB
Average record size in memory838.9 B

Variable types

Text4
Numeric7
Categorical7
DateTime1
URL1

Alerts

Dataset has 62785 (2.7%) duplicate rowsDuplicates
drive is highly overall correlated with typeHigh correlation
odometer is highly overall correlated with predicted_price and 1 other fieldsHigh correlation
predicted_price is highly overall correlated with odometer and 2 other fieldsHigh correlation
price is highly overall correlated with odometer and 1 other fieldsHigh correlation
type is highly overall correlated with driveHigh correlation
year is highly overall correlated with predicted_priceHigh correlation
title is highly imbalanced (77.3%) Imbalance
fuel is highly imbalanced (75.7%) Imbalance
transmission is highly imbalanced (67.8%) Imbalance
make has 47044 (2.1%) missing values Missing
predicted_price has 577806 (25.2%) missing values Missing
residual has 577806 (25.2%) missing values Missing
condition has 369671 (16.1%) missing values Missing
model has 192028 (8.4%) missing values Missing
paint has 623480 (27.2%) missing values Missing
drive has 672749 (29.4%) missing values Missing
type has 434032 (18.9%) missing values Missing

Reproduction

Analysis started2024-12-02 18:35:13.286845
Analysis finished2024-12-02 18:39:08.107901
Duration3 minutes and 54.82 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Distinct413
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size123.7 MiB
2024-12-02T11:39:08.343845image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length7.6498159
Min length2

Characters and Unicode

Total characters17521322
Distinct characters26
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st roworlando
2nd rowatlanta
3rd rowsiouxfalls
4th rowlasvegas
5th rowdallas
ValueCountFrequency (%)
miami 203810
 
8.9%
sfbay 142771
 
6.2%
losangeles 104965
 
4.6%
phoenix 93158
 
4.1%
portland 65872
 
2.9%
seattle 65548
 
2.9%
sandiego 61523
 
2.7%
sacramento 57187
 
2.5%
orangecounty 51898
 
2.3%
dallas 48695
 
2.1%
Other values (403) 1394997
60.9%
2024-12-02T11:39:08.698959image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2004700
11.4%
e 1667454
 
9.5%
n 1557938
 
8.9%
o 1491572
 
8.5%
s 1357311
 
7.7%
i 1251924
 
7.1%
l 1201582
 
6.9%
t 962888
 
5.5%
r 820300
 
4.7%
m 757930
 
4.3%
Other values (16) 4447723
25.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 17521322
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2004700
11.4%
e 1667454
 
9.5%
n 1557938
 
8.9%
o 1491572
 
8.5%
s 1357311
 
7.7%
i 1251924
 
7.1%
l 1201582
 
6.9%
t 962888
 
5.5%
r 820300
 
4.7%
m 757930
 
4.3%
Other values (16) 4447723
25.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 17521322
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2004700
11.4%
e 1667454
 
9.5%
n 1557938
 
8.9%
o 1491572
 
8.5%
s 1357311
 
7.7%
i 1251924
 
7.1%
l 1201582
 
6.9%
t 962888
 
5.5%
r 820300
 
4.7%
m 757930
 
4.3%
Other values (16) 4447723
25.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 17521322
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2004700
11.4%
e 1667454
 
9.5%
n 1557938
 
8.9%
o 1491572
 
8.5%
s 1357311
 
7.7%
i 1251924
 
7.1%
l 1201582
 
6.9%
t 962888
 
5.5%
r 820300
 
4.7%
m 757930
 
4.3%
Other values (16) 4447723
25.4%

price
Real number (ℝ)

High correlation 

Distinct10765
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10589.42
Minimum255
Maximum74999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.5 MiB
2024-12-02T11:39:08.820239image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum255
5-th percentile1995
Q14250
median7000
Q313000
95-th percentile31500
Maximum74999
Range74744
Interquartile range (IQR)8750

Descriptive statistics

Standard deviation10235.364
Coefficient of variation (CV)0.96656514
Kurtosis7.7353662
Mean10589.42
Median Absolute Deviation (MAD)3500
Skewness2.4679172
Sum2.4254261 × 1010
Variance1.0476267 × 108
MonotonicityNot monotonic
2024-12-02T11:39:08.943906image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4500 52927
 
2.3%
3500 52178
 
2.3%
5500 49669
 
2.2%
6500 49295
 
2.2%
5000 42324
 
1.8%
7500 42297
 
1.8%
2500 40796
 
1.8%
8500 36915
 
1.6%
4000 36896
 
1.6%
3000 36063
 
1.6%
Other values (10755) 1851064
80.8%
ValueCountFrequency (%)
255 1
 
< 0.1%
259 11
 
< 0.1%
260 3
 
< 0.1%
265 2
 
< 0.1%
269 3
 
< 0.1%
270 1
 
< 0.1%
275 29
< 0.1%
277 1
 
< 0.1%
279 1
 
< 0.1%
280 3
 
< 0.1%
ValueCountFrequency (%)
74999 98
< 0.1%
74998 3
 
< 0.1%
74995 31
 
< 0.1%
74990 5
 
< 0.1%
74985 1
 
< 0.1%
74980 1
 
< 0.1%
74950 16
 
< 0.1%
74910 1
 
< 0.1%
74900 110
< 0.1%
74888 1
 
< 0.1%

odometer
Real number (ℝ)

High correlation 

Distinct179879
Distinct (%)7.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean133452.78
Minimum1001
Maximum399999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.5 MiB
2024-12-02T11:39:09.070134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1001
5-th percentile30551.15
Q189789
median131000
Q3174000
95-th percentile242150
Maximum399999
Range398998
Interquartile range (IQR)84211

Descriptive statistics

Standard deviation63758.95
Coefficient of variation (CV)0.47776412
Kurtosis0.27523691
Mean133452.78
Median Absolute Deviation (MAD)42000
Skewness0.3818283
Sum3.0566345 × 1011
Variance4.0652037 × 109
MonotonicityNot monotonic
2024-12-02T11:39:09.208270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200000 36830
 
1.6%
150000 30157
 
1.3%
160000 27103
 
1.2%
140000 25492
 
1.1%
180000 24918
 
1.1%
170000 24282
 
1.1%
130000 23842
 
1.0%
120000 21534
 
0.9%
100000 18619
 
0.8%
190000 18199
 
0.8%
Other values (179869) 2039448
89.0%
ValueCountFrequency (%)
1001 6
< 0.1%
1003 2
 
< 0.1%
1004 4
 
< 0.1%
1005 3
 
< 0.1%
1006 5
< 0.1%
1007 3
 
< 0.1%
1008 9
< 0.1%
1009 3
 
< 0.1%
1010 10
< 0.1%
1012 1
 
< 0.1%
ValueCountFrequency (%)
399999 4
< 0.1%
399990 1
 
< 0.1%
399896 1
 
< 0.1%
399842 1
 
< 0.1%
399788 1
 
< 0.1%
399700 1
 
< 0.1%
399699 1
 
< 0.1%
399695 1
 
< 0.1%
399600 1
 
< 0.1%
399559 1
 
< 0.1%

year
Real number (ℝ)

High correlation 

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2008.2009
Minimum1974
Maximum2025
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size17.5 MiB
2024-12-02T11:39:09.336936image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1974
5-th percentile1993
Q12004
median2009
Q32014
95-th percentile2019
Maximum2025
Range51
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.3771385
Coefficient of variation (CV)0.0041714645
Kurtosis1.9719509
Mean2008.2009
Median Absolute Deviation (MAD)5
Skewness-1.0987996
Sum4.5996314 × 109
Variance70.17645
MonotonicityNot monotonic
2024-12-02T11:39:09.456058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2013 130674
 
5.7%
2008 128567
 
5.6%
2007 127225
 
5.6%
2012 126240
 
5.5%
2014 120326
 
5.3%
2006 117439
 
5.1%
2011 115067
 
5.0%
2015 110476
 
4.8%
2010 104005
 
4.5%
2005 101884
 
4.4%
Other values (42) 1108521
48.4%
ValueCountFrequency (%)
1974 4221
0.2%
1975 2951
0.1%
1976 4042
0.2%
1977 4653
0.2%
1978 5202
0.2%
1979 5979
0.3%
1980 3472
0.2%
1981 2949
0.1%
1982 3375
0.1%
1983 3689
0.2%
ValueCountFrequency (%)
2025 240
 
< 0.1%
2024 4941
 
0.2%
2023 15980
 
0.7%
2022 23683
 
1.0%
2021 31404
 
1.4%
2020 37022
 
1.6%
2019 52737
2.3%
2018 64096
2.8%
2017 81557
3.6%
2016 93361
4.1%

long
Real number (ℝ)

Distinct306755
Distinct (%)13.4%
Missing136
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean-100.61135
Minimum-177.00763
Maximum142.12326
Zeros1846
Zeros (%)0.1%
Negative2288416
Negative (%)99.9%
Memory size17.5 MiB
2024-12-02T11:39:09.570448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-177.00763
5-th percentile-122.6801
Q1-118.3507
median-97.9122
Q3-80.9288
95-th percentile-73.709983
Maximum142.12326
Range319.13089
Interquartile range (IQR)37.4219

Descriptive statistics

Standard deviation19.928225
Coefficient of variation (CV)-0.19807134
Kurtosis-0.18559081
Mean-100.61135
Median Absolute Deviation (MAD)18.4498
Skewness-0.12791197
Sum-2.3042896 × 108
Variance397.13414
MonotonicityNot monotonic
2024-12-02T11:39:09.685218image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-80.2926 5319
 
0.2%
-80.2264 5166
 
0.2%
-80.1407 3886
 
0.2%
-80.3165 3618
 
0.2%
-80.1157 3094
 
0.1%
-80.403 2943
 
0.1%
-80.4085 2652
 
0.1%
-80.1891 2583
 
0.1%
-80.2153 2566
 
0.1%
-80.2092 2491
 
0.1%
Other values (306745) 2255970
98.5%
ValueCountFrequency (%)
-177.007631 1
 
< 0.1%
-175.869141 1
 
< 0.1%
-166.538642 1
 
< 0.1%
-166.1151 1
 
< 0.1%
-165.961268 1
 
< 0.1%
-162.7211 1
 
< 0.1%
-162.685547 1
 
< 0.1%
-161.990162 3
< 0.1%
-161.8749 2
< 0.1%
-159.8251 1
 
< 0.1%
ValueCountFrequency (%)
142.12326 1
 
< 0.1%
139.6917 2
< 0.1%
139.3485 1
 
< 0.1%
136.8326 1
 
< 0.1%
128.838981 1
 
< 0.1%
127.7586 2
< 0.1%
127.0706 2
< 0.1%
126.9775 1
 
< 0.1%
126.92395 4
< 0.1%
120.5833 1
 
< 0.1%

lat
Real number (ℝ)

Distinct306497
Distinct (%)13.4%
Missing136
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean36.578113
Minimum-82.921058
Maximum142.58258
Zeros1852
Zeros (%)0.1%
Negative231
Negative (%)< 0.1%
Memory size17.5 MiB
2024-12-02T11:39:09.805971image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-82.921058
5-th percentile25.983891
Q133.1101
median36.963687
Q340.964616
95-th percentile46.871
Maximum142.58258
Range225.50363
Interquartile range (IQR)7.8545162

Descriptive statistics

Standard deviation6.5174326
Coefficient of variation (CV)0.17817848
Kurtosis2.9795332
Mean36.578113
Median Absolute Deviation (MAD)3.9343865
Skewness-0.29118532
Sum83774412
Variance42.476928
MonotonicityNot monotonic
2024-12-02T11:39:09.921317image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25.8301 5319
 
0.2%
26.1122 5171
 
0.2%
26.0218 4218
 
0.2%
25.985 3841
 
0.2%
26.2785 3094
 
0.1%
26.152 2865
 
0.1%
25.6694 2652
 
0.1%
25.9894 2554
 
0.1%
26.2674 2487
 
0.1%
27.3215 2263
 
0.1%
Other values (306487) 2255824
98.5%
ValueCountFrequency (%)
-82.921058 1
< 0.1%
-80.172912 1
< 0.1%
-75.747631 1
< 0.1%
-67.980238 1
< 0.1%
-66.787821 1
< 0.1%
-62.712724 1
< 0.1%
-62.428643 1
< 0.1%
-62.383597 1
< 0.1%
-60.319107 1
< 0.1%
-55.379769 1
< 0.1%
ValueCountFrequency (%)
142.582577 1
 
< 0.1%
71.683788 1
 
< 0.1%
71.41821 1
 
< 0.1%
66.742308 1
 
< 0.1%
66.5656 1
 
< 0.1%
65.168 1
 
< 0.1%
65.0907 1
 
< 0.1%
65.07 17
< 0.1%
65.002647 1
 
< 0.1%
64.999013 1
 
< 0.1%

make
Text

Missing 

Distinct65
Distinct (%)< 0.1%
Missing47044
Missing (%)2.1%
Memory size118.8 MiB
2024-12-02T11:39:10.073880image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length5.8706425
Min length3

Characters and Unicode

Total characters13170082
Distinct characters27
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st rowfreightliner
2nd rowbuick
3rd rowchevrolet
4th rowpontiac
5th rowaudi
ValueCountFrequency (%)
ford 343923
15.2%
toyota 260562
 
11.5%
chevrolet 256525
 
11.4%
honda 161340
 
7.2%
nissan 129465
 
5.7%
jeep 92190
 
4.1%
bmw 84250
 
3.7%
mercedes-benz 69607
 
3.1%
gmc 68722
 
3.0%
ram 68650
 
3.0%
Other values (58) 720945
32.0%
2024-12-02T11:39:10.325053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 1508165
 
11.5%
e 1243755
 
9.4%
a 1126233
 
8.6%
r 981529
 
7.5%
t 861336
 
6.5%
d 859238
 
6.5%
n 737347
 
5.6%
s 621338
 
4.7%
c 594419
 
4.5%
l 539122
 
4.1%
Other values (17) 4097600
31.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 13170082
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 1508165
 
11.5%
e 1243755
 
9.4%
a 1126233
 
8.6%
r 981529
 
7.5%
t 861336
 
6.5%
d 859238
 
6.5%
n 737347
 
5.6%
s 621338
 
4.7%
c 594419
 
4.5%
l 539122
 
4.1%
Other values (17) 4097600
31.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 13170082
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 1508165
 
11.5%
e 1243755
 
9.4%
a 1126233
 
8.6%
r 981529
 
7.5%
t 861336
 
6.5%
d 859238
 
6.5%
n 737347
 
5.6%
s 621338
 
4.7%
c 594419
 
4.5%
l 539122
 
4.1%
Other values (17) 4097600
31.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 13170082
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 1508165
 
11.5%
e 1243755
 
9.4%
a 1126233
 
8.6%
r 981529
 
7.5%
t 861336
 
6.5%
d 859238
 
6.5%
n 737347
 
5.6%
s 621338
 
4.7%
c 594419
 
4.5%
l 539122
 
4.1%
Other values (17) 4097600
31.1%

title
Categorical

Imbalance 

Distinct6
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size118.3 MiB
clean
2080940 
rebuilt
 
103170
salvage
 
77702
lien
 
20006
missing
 
6687

Length

Max length10
Median length5
Mean length5.1592312
Min length4

Characters and Unicode

Total characters11816827
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowclean
2nd rowclean
3rd rowrebuilt
4th rowclean
5th rowclean

Common Values

ValueCountFrequency (%)
clean 2080940
90.9%
rebuilt 103170
 
4.5%
salvage 77702
 
3.4%
lien 20006
 
0.9%
missing 6687
 
0.3%
parts only 1919
 
0.1%

Length

2024-12-02T11:39:10.439744image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-02T11:39:10.542467image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
clean 2080940
90.8%
rebuilt 103170
 
4.5%
salvage 77702
 
3.4%
lien 20006
 
0.9%
missing 6687
 
0.3%
parts 1919
 
0.1%
only 1919
 
0.1%

Most occurring characters

ValueCountFrequency (%)
l 2283737
19.3%
e 2281818
19.3%
a 2238263
18.9%
n 2109552
17.9%
c 2080940
17.6%
i 136550
 
1.2%
t 105089
 
0.9%
r 105089
 
0.9%
u 103170
 
0.9%
b 103170
 
0.9%
Other values (8) 269449
 
2.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11816827
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
l 2283737
19.3%
e 2281818
19.3%
a 2238263
18.9%
n 2109552
17.9%
c 2080940
17.6%
i 136550
 
1.2%
t 105089
 
0.9%
r 105089
 
0.9%
u 103170
 
0.9%
b 103170
 
0.9%
Other values (8) 269449
 
2.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11816827
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
l 2283737
19.3%
e 2281818
19.3%
a 2238263
18.9%
n 2109552
17.9%
c 2080940
17.6%
i 136550
 
1.2%
t 105089
 
0.9%
r 105089
 
0.9%
u 103170
 
0.9%
b 103170
 
0.9%
Other values (8) 269449
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11816827
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
l 2283737
19.3%
e 2281818
19.3%
a 2238263
18.9%
n 2109552
17.9%
c 2080940
17.6%
i 136550
 
1.2%
t 105089
 
0.9%
r 105089
 
0.9%
u 103170
 
0.9%
b 103170
 
0.9%
Other values (8) 269449
 
2.3%

predicted_price
Real number (ℝ)

High correlation  Missing 

Distinct881482
Distinct (%)51.5%
Missing577806
Missing (%)25.2%
Infinite0
Infinite (%)0.0%
Mean9636.5497
Minimum-121819.73
Maximum175218.63
Zeros0
Zeros (%)0.0%
Negative34034
Negative (%)1.5%
Memory size17.5 MiB
2024-12-02T11:39:10.652941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-121819.73
5-th percentile1318.9146
Q13974.4492
median6950.429
Q312119.948
95-th percentile27921.64
Maximum175218.63
Range297038.36
Interquartile range (IQR)8145.4984

Descriptive statistics

Standard deviation9228.5677
Coefficient of variation (CV)0.95766306
Kurtosis10.302222
Mean9636.5497
Median Absolute Deviation (MAD)3584.9327
Skewness2.5019846
Sum1.6503729 × 1010
Variance85166462
MonotonicityNot monotonic
2024-12-02T11:39:10.765834image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
5963.142062 365
 
< 0.1%
5206.482999 325
 
< 0.1%
12464.03675 310
 
< 0.1%
5614.342006 215
 
< 0.1%
4978.933092 185
 
< 0.1%
7479.966291 177
 
< 0.1%
6482.27056 165
 
< 0.1%
28340.80192 165
 
< 0.1%
15873.14682 163
 
< 0.1%
7034.002231 155
 
< 0.1%
Other values (881472) 1710393
74.7%
(Missing) 577806
 
25.2%
ValueCountFrequency (%)
-121819.73 1
 
< 0.1%
-112894.7437 2
 
< 0.1%
-110346.7735 1
 
< 0.1%
-87851.72981 1
 
< 0.1%
-79342.24351 1
 
< 0.1%
-79129.65846 1
 
< 0.1%
-76550.57958 5
< 0.1%
-70779.46514 1
 
< 0.1%
-68658.36891 1
 
< 0.1%
-68222.09689 1
 
< 0.1%
ValueCountFrequency (%)
175218.6349 1
< 0.1%
174149.3679 1
< 0.1%
166346.281 1
< 0.1%
163898.331 1
< 0.1%
145138.8127 1
< 0.1%
137544.701 1
< 0.1%
136504.9896 1
< 0.1%
135853.7222 1
< 0.1%
132849.0895 1
< 0.1%
132441.7556 1
< 0.1%

residual
Real number (ℝ)

Missing 

Distinct1250957
Distinct (%)73.0%
Missing577806
Missing (%)25.2%
Infinite0
Infinite (%)0.0%
Mean930.84842
Minimum-173528.63
Maximum145629.66
Zeros0
Zeros (%)0.0%
Negative701065
Negative (%)30.6%
Memory size17.5 MiB
2024-12-02T11:39:10.885010image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-173528.63
5-th percentile-5636.294
Q1-1118.6769
median542.07632
Q32474.398
95-th percentile9099.9732
Maximum145629.66
Range319158.29
Interquartile range (IQR)3593.0749

Descriptive statistics

Standard deviation5451.9053
Coefficient of variation (CV)5.8569207
Kurtosis23.218567
Mean930.84842
Median Absolute Deviation (MAD)1781.6379
Skewness0.64006142
Sum1.5941878 × 109
Variance29723272
MonotonicityNot monotonic
2024-12-02T11:39:11.001556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16035.96325 306
 
< 0.1%
1420.033709 175
 
< 0.1%
526.8579378 170
 
< 0.1%
93.517001 165
 
< 0.1%
1026.853182 160
 
< 0.1%
-13208.73135 140
 
< 0.1%
430.6721447 135
 
< 0.1%
-502.4700821 135
 
< 0.1%
1902.713124 132
 
< 0.1%
-7724.816742 125
 
< 0.1%
Other values (1250947) 1710975
74.7%
(Missing) 577806
 
25.2%
ValueCountFrequency (%)
-173528.6349 1
< 0.1%
-172649.3679 1
< 0.1%
-143448.8127 1
< 0.1%
-129441.7556 1
< 0.1%
-117888.2509 1
< 0.1%
-116762.4411 1
< 0.1%
-105853.7222 1
< 0.1%
-99356.95753 1
< 0.1%
-97346.28101 1
< 0.1%
-96898.33099 1
< 0.1%
ValueCountFrequency (%)
145629.6585 1
 
< 0.1%
127894.7437 2
 
< 0.1%
123819.73 1
 
< 0.1%
113050.5796 5
< 0.1%
111096.7735 1
 
< 0.1%
105351.7298 1
 
< 0.1%
97458.36891 1
 
< 0.1%
97068.20604 1
 
< 0.1%
91842.24351 1
 
< 0.1%
81779.46514 1
 
< 0.1%
Distinct1856420
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Memory size17.5 MiB
Minimum2023-12-03 08:00:27+00:00
Maximum2024-12-01 20:08:47+00:00
2024-12-02T11:39:11.121855image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:39:11.238882image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

condition
Categorical

Missing 

Distinct6
Distinct (%)< 0.1%
Missing369671
Missing (%)16.1%
Memory size121.6 MiB
excellent
806283 
good
713692 
like new
232055 
fair
143875 
salvage
 
13279

Length

Max length9
Median length8
Mean length6.5968477
Min length3

Characters and Unicode

Total characters12670915
Distinct characters18
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowgood
2nd rowexcellent
3rd rowlike new
4th rowexcellent
5th rowgood

Common Values

ValueCountFrequency (%)
excellent 806283
35.2%
good 713692
31.2%
like new 232055
 
10.1%
fair 143875
 
6.3%
salvage 13279
 
0.6%
new 11569
 
0.5%
(Missing) 369671
16.1%

Length

2024-12-02T11:39:11.353490image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-02T11:39:11.454522image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
excellent 806283
37.5%
good 713692
33.2%
new 243624
 
11.3%
like 232055
 
10.8%
fair 143875
 
6.7%
salvage 13279
 
0.6%

Most occurring characters

ValueCountFrequency (%)
e 2907807
22.9%
l 1857900
14.7%
o 1427384
11.3%
n 1049907
 
8.3%
x 806283
 
6.4%
c 806283
 
6.4%
t 806283
 
6.4%
g 726971
 
5.7%
d 713692
 
5.6%
i 375930
 
3.0%
Other values (8) 1192475
9.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12670915
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 2907807
22.9%
l 1857900
14.7%
o 1427384
11.3%
n 1049907
 
8.3%
x 806283
 
6.4%
c 806283
 
6.4%
t 806283
 
6.4%
g 726971
 
5.7%
d 713692
 
5.6%
i 375930
 
3.0%
Other values (8) 1192475
9.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12670915
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 2907807
22.9%
l 1857900
14.7%
o 1427384
11.3%
n 1049907
 
8.3%
x 806283
 
6.4%
c 806283
 
6.4%
t 806283
 
6.4%
g 726971
 
5.7%
d 713692
 
5.6%
i 375930
 
3.0%
Other values (8) 1192475
9.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12670915
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 2907807
22.9%
l 1857900
14.7%
o 1427384
11.3%
n 1049907
 
8.3%
x 806283
 
6.4%
c 806283
 
6.4%
t 806283
 
6.4%
g 726971
 
5.7%
d 713692
 
5.6%
i 375930
 
3.0%
Other values (8) 1192475
9.4%

url
URL

Distinct1998952
Distinct (%)87.3%
Missing0
Missing (%)0.0%
Memory size297.4 MiB
https://miami.craigslist.org/mdc/cto/d/miami-2018-ford-mustang-convertible/7762499156.html
 
110
https://miami.craigslist.org/pbc/cto/d/boca-raton-2012-kia-rio/7762474333.html
 
95
https://miami.craigslist.org/pbc/cto/d/deerfield-beach-2005-scion-xb-in-fair/7758116281.html
 
95
https://miami.craigslist.org/pbc/cto/d/boca-raton-2006-gmc-sierra-1500-sle/7759974947.html
 
95
https://miami.craigslist.org/mdc/cto/d/miami-2005-lexus-ls430/7761198368.html
 
90
Other values (1998947)
2289939 
ValueCountFrequency (%)
https://miami.craigslist.org/mdc/cto/d/miami-2018-ford-mustang-convertible/7762499156.html 110
 
< 0.1%
https://miami.craigslist.org/pbc/cto/d/boca-raton-2012-kia-rio/7762474333.html 95
 
< 0.1%
https://miami.craigslist.org/pbc/cto/d/deerfield-beach-2005-scion-xb-in-fair/7758116281.html 95
 
< 0.1%
https://miami.craigslist.org/pbc/cto/d/boca-raton-2006-gmc-sierra-1500-sle/7759974947.html 95
 
< 0.1%
https://miami.craigslist.org/mdc/cto/d/miami-2005-lexus-ls430/7761198368.html 90
 
< 0.1%
https://miami.craigslist.org/mdc/cto/d/miami-2011-ford-mustang/7760134705.html 90
 
< 0.1%
https://miami.craigslist.org/pbc/cto/d/lake-worth-2004-bmw-325i-ice-cold-ac/7754977134.html 85
 
< 0.1%
https://miami.craigslist.org/brw/cto/d/fort-lauderdale-lexus-ct200h-2015/7759810242.html 85
 
< 0.1%
https://miami.craigslist.org/mdc/cto/d/miami-2019-gmc-acadia/7760605245.html 85
 
< 0.1%
https://miami.craigslist.org/mdc/cto/d/hallandale-hyundai-sonata-2017/7755236994.html 80
 
< 0.1%
Other values (1998942) 2289514
> 99.9%
ValueCountFrequency (%)
https 2290424
100.0%
ValueCountFrequency (%)
miami.craigslist.org 203815
 
8.9%
sfbay.craigslist.org 142970
 
6.2%
losangeles.craigslist.org 104976
 
4.6%
phoenix.craigslist.org 93184
 
4.1%
portland.craigslist.org 65886
 
2.9%
seattle.craigslist.org 65580
 
2.9%
sandiego.craigslist.org 61539
 
2.7%
sacramento.craigslist.org 57269
 
2.5%
orangecounty.craigslist.org 51898
 
2.3%
dallas.craigslist.org 48703
 
2.1%
Other values (405) 1394604
60.9%
ValueCountFrequency (%)
/mdc/cto/d/miami-2018-ford-mustang-convertible/7762499156.html 110
 
< 0.1%
/pbc/cto/d/boca-raton-2012-kia-rio/7762474333.html 95
 
< 0.1%
/pbc/cto/d/deerfield-beach-2005-scion-xb-in-fair/7758116281.html 95
 
< 0.1%
/pbc/cto/d/boca-raton-2006-gmc-sierra-1500-sle/7759974947.html 95
 
< 0.1%
/mdc/cto/d/miami-2005-lexus-ls430/7761198368.html 90
 
< 0.1%
/mdc/cto/d/miami-2011-ford-mustang/7760134705.html 90
 
< 0.1%
/pbc/cto/d/lake-worth-2004-bmw-325i-ice-cold-ac/7754977134.html 85
 
< 0.1%
/brw/cto/d/fort-lauderdale-lexus-ct200h-2015/7759810242.html 85
 
< 0.1%
/mdc/cto/d/miami-2019-gmc-acadia/7760605245.html 85
 
< 0.1%
/mdc/cto/d/hallandale-hyundai-sonata-2017/7755236994.html 80
 
< 0.1%
Other values (1998942) 2289514
> 99.9%
ValueCountFrequency (%)
2290424
100.0%
ValueCountFrequency (%)
2290424
100.0%

state
Text

Distinct51
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size125.5 MiB
2024-12-02T11:39:11.622069image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length8.4478939
Min length4

Characters and Unicode

Total characters19349259
Distinct characters46
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFlorida
2nd rowGeorgia
3rd rowSouth Dakota
4th rowNevada
5th rowTexas
ValueCountFrequency (%)
california 542610
21.0%
florida 295453
 
11.4%
new 177884
 
6.9%
texas 152277
 
5.9%
arizona 128754
 
5.0%
washington 104607
 
4.0%
oregon 102795
 
4.0%
york 95515
 
3.7%
colorado 76948
 
3.0%
carolina 59556
 
2.3%
Other values (45) 853078
32.9%
2024-12-02T11:39:11.898190image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 2683483
13.9%
i 2413530
12.5%
o 2010117
10.4%
n 1582668
 
8.2%
r 1552795
 
8.0%
l 1172578
 
6.1%
e 902435
 
4.7%
s 802271
 
4.1%
C 718589
 
3.7%
f 565667
 
2.9%
Other values (36) 4945126
25.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19349259
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 2683483
13.9%
i 2413530
12.5%
o 2010117
10.4%
n 1582668
 
8.2%
r 1552795
 
8.0%
l 1172578
 
6.1%
e 902435
 
4.7%
s 802271
 
4.1%
C 718589
 
3.7%
f 565667
 
2.9%
Other values (36) 4945126
25.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19349259
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 2683483
13.9%
i 2413530
12.5%
o 2010117
10.4%
n 1582668
 
8.2%
r 1552795
 
8.0%
l 1172578
 
6.1%
e 902435
 
4.7%
s 802271
 
4.1%
C 718589
 
3.7%
f 565667
 
2.9%
Other values (36) 4945126
25.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19349259
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 2683483
13.9%
i 2413530
12.5%
o 2010117
10.4%
n 1582668
 
8.2%
r 1552795
 
8.0%
l 1172578
 
6.1%
e 902435
 
4.7%
s 802271
 
4.1%
C 718589
 
3.7%
f 565667
 
2.9%
Other values (36) 4945126
25.6%

model
Text

Missing 

Distinct1287
Distinct (%)0.1%
Missing192028
Missing (%)8.4%
Memory size115.2 MiB
2024-12-02T11:39:12.150741image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Length

Max length31
Median length26
Mean length5.6316782
Min length1

Characters and Unicode

Total characters11817491
Distinct characters43
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)< 0.1%

Sample

1st rowsprinter 2500
2nd rowencore
3rd rowcolorado
4th rowsolstice
5th rowq5
ValueCountFrequency (%)
1500 72009
 
3.2%
f150 69513
 
3.1%
silverado 53250
 
2.3%
camry 45510
 
2.0%
accord 44643
 
2.0%
r 41880
 
1.8%
2500 41112
 
1.8%
civic 39560
 
1.7%
wrangler 38069
 
1.7%
f250 31990
 
1.4%
Other values (940) 1789701
78.9%
2024-12-02T11:39:12.525758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 1197265
 
10.1%
r 1162904
 
9.8%
e 1025594
 
8.7%
o 740805
 
6.3%
c 683820
 
5.8%
t 610484
 
5.2%
s 601543
 
5.1%
i 570802
 
4.8%
n 560962
 
4.7%
0 512209
 
4.3%
Other values (33) 4151103
35.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 11817491
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 1197265
 
10.1%
r 1162904
 
9.8%
e 1025594
 
8.7%
o 740805
 
6.3%
c 683820
 
5.8%
t 610484
 
5.2%
s 601543
 
5.1%
i 570802
 
4.8%
n 560962
 
4.7%
0 512209
 
4.3%
Other values (33) 4151103
35.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 11817491
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 1197265
 
10.1%
r 1162904
 
9.8%
e 1025594
 
8.7%
o 740805
 
6.3%
c 683820
 
5.8%
t 610484
 
5.2%
s 601543
 
5.1%
i 570802
 
4.8%
n 560962
 
4.7%
0 512209
 
4.3%
Other values (33) 4151103
35.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 11817491
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 1197265
 
10.1%
r 1162904
 
9.8%
e 1025594
 
8.7%
o 740805
 
6.3%
c 683820
 
5.8%
t 610484
 
5.2%
s 601543
 
5.1%
i 570802
 
4.8%
n 560962
 
4.7%
0 512209
 
4.3%
Other values (33) 4151103
35.1%

paint
Categorical

Missing 

Distinct12
Distinct (%)< 0.1%
Missing623480
Missing (%)27.2%
Memory size118.8 MiB
white
409252 
black
290438 
silver
238523 
grey
215967 
blue
179710 
Other values (7)
333054 

Length

Max length6
Median length5
Mean length4.7566985
Min length3

Characters and Unicode

Total characters7929150
Distinct characters21
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowwhite
2nd rowred
3rd rowbrown
4th rowgreen
5th rowcustom

Common Values

ValueCountFrequency (%)
white 409252
17.9%
black 290438
12.7%
silver 238523
 
10.4%
grey 215967
 
9.4%
blue 179710
 
7.8%
red 157710
 
6.9%
green 60055
 
2.6%
brown 48285
 
2.1%
custom 36398
 
1.6%
yellow 15178
 
0.7%
Other values (2) 15428
 
0.7%
(Missing) 623480
27.2%

Length

2024-12-02T11:39:12.640211image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
white 409252
24.6%
black 290438
17.4%
silver 238523
14.3%
grey 215967
13.0%
blue 179710
10.8%
red 157710
 
9.5%
green 60055
 
3.6%
brown 48285
 
2.9%
custom 36398
 
2.2%
yellow 15178
 
0.9%
Other values (2) 15428
 
0.9%

Most occurring characters

ValueCountFrequency (%)
e 1351878
17.0%
l 744883
 
9.4%
r 735968
 
9.3%
i 647775
 
8.2%
b 518433
 
6.5%
w 472715
 
6.0%
t 445650
 
5.6%
h 409252
 
5.2%
c 326836
 
4.1%
a 300010
 
3.8%
Other values (11) 1975750
24.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7929150
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 1351878
17.0%
l 744883
 
9.4%
r 735968
 
9.3%
i 647775
 
8.2%
b 518433
 
6.5%
w 472715
 
6.0%
t 445650
 
5.6%
h 409252
 
5.2%
c 326836
 
4.1%
a 300010
 
3.8%
Other values (11) 1975750
24.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7929150
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 1351878
17.0%
l 744883
 
9.4%
r 735968
 
9.3%
i 647775
 
8.2%
b 518433
 
6.5%
w 472715
 
6.0%
t 445650
 
5.6%
h 409252
 
5.2%
c 326836
 
4.1%
a 300010
 
3.8%
Other values (11) 1975750
24.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7929150
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 1351878
17.0%
l 744883
 
9.4%
r 735968
 
9.3%
i 647775
 
8.2%
b 518433
 
6.5%
w 472715
 
6.0%
t 445650
 
5.6%
h 409252
 
5.2%
c 326836
 
4.1%
a 300010
 
3.8%
Other values (11) 1975750
24.9%

fuel
Categorical

Imbalance 

Distinct5
Distinct (%)< 0.1%
Missing12
Missing (%)< 0.1%
Memory size114.2 MiB
gas
2083719 
diesel
 
129600
hybrid
 
50482
electric
 
21067
other
 
5544

Length

Max length8
Median length3
Mean length3.2867034
Min length3

Characters and Unicode

Total characters7527905
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowdiesel
2nd rowgas
3rd rowgas
4th rowgas
5th rowgas

Common Values

ValueCountFrequency (%)
gas 2083719
91.0%
diesel 129600
 
5.7%
hybrid 50482
 
2.2%
electric 21067
 
0.9%
other 5544
 
0.2%
(Missing) 12
 
< 0.1%

Length

2024-12-02T11:39:12.738895image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-02T11:39:12.824963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
gas 2083719
91.0%
diesel 129600
 
5.7%
hybrid 50482
 
2.2%
electric 21067
 
0.9%
other 5544
 
0.2%

Most occurring characters

ValueCountFrequency (%)
s 2213319
29.4%
g 2083719
27.7%
a 2083719
27.7%
e 306878
 
4.1%
i 201149
 
2.7%
d 180082
 
2.4%
l 150667
 
2.0%
r 77093
 
1.0%
h 56026
 
0.7%
y 50482
 
0.7%
Other values (4) 124771
 
1.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 7527905
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
s 2213319
29.4%
g 2083719
27.7%
a 2083719
27.7%
e 306878
 
4.1%
i 201149
 
2.7%
d 180082
 
2.4%
l 150667
 
2.0%
r 77093
 
1.0%
h 56026
 
0.7%
y 50482
 
0.7%
Other values (4) 124771
 
1.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 7527905
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
s 2213319
29.4%
g 2083719
27.7%
a 2083719
27.7%
e 306878
 
4.1%
i 201149
 
2.7%
d 180082
 
2.4%
l 150667
 
2.0%
r 77093
 
1.0%
h 56026
 
0.7%
y 50482
 
0.7%
Other values (4) 124771
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 7527905
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
s 2213319
29.4%
g 2083719
27.7%
a 2083719
27.7%
e 306878
 
4.1%
i 201149
 
2.7%
d 180082
 
2.4%
l 150667
 
2.0%
r 77093
 
1.0%
h 56026
 
0.7%
y 50482
 
0.7%
Other values (4) 124771
 
1.7%

drive
Categorical

High correlation  Missing 

Distinct3
Distinct (%)< 0.1%
Missing672749
Missing (%)29.4%
Memory size116.2 MiB
4wd
643667 
fwd
572393 
rwd
401615 

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters4853025
Distinct characters5
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowfwd
2nd row4wd
3rd row4wd
4th row4wd
5th rowfwd

Common Values

ValueCountFrequency (%)
4wd 643667
28.1%
fwd 572393
25.0%
rwd 401615
17.5%
(Missing) 672749
29.4%

Length

2024-12-02T11:39:12.920075image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-02T11:39:13.010381image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
4wd 643667
39.8%
fwd 572393
35.4%
rwd 401615
24.8%

Most occurring characters

ValueCountFrequency (%)
w 1617675
33.3%
d 1617675
33.3%
4 643667
 
13.3%
f 572393
 
11.8%
r 401615
 
8.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4853025
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
w 1617675
33.3%
d 1617675
33.3%
4 643667
 
13.3%
f 572393
 
11.8%
r 401615
 
8.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4853025
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
w 1617675
33.3%
d 1617675
33.3%
4 643667
 
13.3%
f 572393
 
11.8%
r 401615
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4853025
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
w 1617675
33.3%
d 1617675
33.3%
4 643667
 
13.3%
f 572393
 
11.8%
r 401615
 
8.3%

type
Categorical

High correlation  Missing 

Distinct14
Distinct (%)< 0.1%
Missing434032
Missing (%)18.9%
Memory size118.8 MiB
SUV
502396 
sedan
502343 
pickup
236256 
truck
150495 
coupe
101984 
Other values (9)
362918 

Length

Max length11
Median length9
Mean length5.0273207
Min length3

Characters and Unicode

Total characters9332678
Distinct characters25
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowvan
2nd rowtruck
3rd rowSUV
4th rowSUV
5th rowSUV

Common Values

ValueCountFrequency (%)
SUV 502396
21.9%
sedan 502343
21.9%
pickup 236256
10.3%
truck 150495
 
6.6%
coupe 101984
 
4.5%
hatchback 100873
 
4.4%
convertible 72582
 
3.2%
van 70396
 
3.1%
minivan 49180
 
2.1%
wagon 33466
 
1.5%
Other values (4) 36421
 
1.6%
(Missing) 434032
18.9%

Length

2024-12-02T11:39:13.118090image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
suv 502396
27.1%
sedan 502343
27.1%
pickup 236256
12.7%
truck 150495
 
8.1%
coupe 101984
 
5.5%
hatchback 100873
 
5.4%
convertible 72582
 
3.9%
van 70396
 
3.8%
minivan 49180
 
2.6%
wagon 33466
 
1.8%
Other values (4) 36421
 
2.0%

Most occurring characters

ValueCountFrequency (%)
a 870503
 
9.3%
n 785851
 
8.4%
e 768343
 
8.2%
c 763063
 
8.2%
p 574496
 
6.2%
d 511363
 
5.5%
s 506540
 
5.4%
S 502396
 
5.4%
V 502396
 
5.4%
U 502396
 
5.4%
Other values (15) 3045331
32.6%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9332678
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 870503
 
9.3%
n 785851
 
8.4%
e 768343
 
8.2%
c 763063
 
8.2%
p 574496
 
6.2%
d 511363
 
5.5%
s 506540
 
5.4%
S 502396
 
5.4%
V 502396
 
5.4%
U 502396
 
5.4%
Other values (15) 3045331
32.6%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9332678
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 870503
 
9.3%
n 785851
 
8.4%
e 768343
 
8.2%
c 763063
 
8.2%
p 574496
 
6.2%
d 511363
 
5.5%
s 506540
 
5.4%
S 502396
 
5.4%
V 502396
 
5.4%
U 502396
 
5.4%
Other values (15) 3045331
32.6%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9332678
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 870503
 
9.3%
n 785851
 
8.4%
e 768343
 
8.2%
c 763063
 
8.2%
p 574496
 
6.2%
d 511363
 
5.5%
s 506540
 
5.4%
S 502396
 
5.4%
V 502396
 
5.4%
U 502396
 
5.4%
Other values (15) 3045331
32.6%

transmission
Categorical

Imbalance 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size126.0 MiB
automatic
2056290 
manual
219656 
other
 
14478

Length

Max length9
Median length9
Mean length8.6870099
Min length5

Characters and Unicode

Total characters19896936
Distinct characters12
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowautomatic
2nd rowautomatic
3rd rowautomatic
4th rowautomatic
5th rowautomatic

Common Values

ValueCountFrequency (%)
automatic 2056290
89.8%
manual 219656
 
9.6%
other 14478
 
0.6%

Length

2024-12-02T11:39:13.230110image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-12-02T11:39:13.319967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
automatic 2056290
89.8%
manual 219656
 
9.6%
other 14478
 
0.6%

Most occurring characters

ValueCountFrequency (%)
a 4551892
22.9%
t 4127058
20.7%
u 2275946
11.4%
m 2275946
11.4%
o 2070768
10.4%
i 2056290
10.3%
c 2056290
10.3%
n 219656
 
1.1%
l 219656
 
1.1%
h 14478
 
0.1%
Other values (2) 28956
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 19896936
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 4551892
22.9%
t 4127058
20.7%
u 2275946
11.4%
m 2275946
11.4%
o 2070768
10.4%
i 2056290
10.3%
c 2056290
10.3%
n 219656
 
1.1%
l 219656
 
1.1%
h 14478
 
0.1%
Other values (2) 28956
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 19896936
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 4551892
22.9%
t 4127058
20.7%
u 2275946
11.4%
m 2275946
11.4%
o 2070768
10.4%
i 2056290
10.3%
c 2056290
10.3%
n 219656
 
1.1%
l 219656
 
1.1%
h 14478
 
0.1%
Other values (2) 28956
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 19896936
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 4551892
22.9%
t 4127058
20.7%
u 2275946
11.4%
m 2275946
11.4%
o 2070768
10.4%
i 2056290
10.3%
c 2056290
10.3%
n 219656
 
1.1%
l 219656
 
1.1%
h 14478
 
0.1%
Other values (2) 28956
 
0.1%

Interactions

2024-12-02T11:38:46.724210image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:33.181033image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:35.495251image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:37.800005image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:40.117357image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:42.442061image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:44.726900image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:47.037460image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:33.549363image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:35.815387image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:38.152058image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:40.472324image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:42.796334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:45.046067image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:47.316732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:33.888448image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:36.167437image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:38.467206image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:40.821380image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:43.186257image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:45.326813image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:47.601009image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:34.222573image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:36.507519image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:38.794323image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:41.172433image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:43.533344image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:45.591159image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:47.882922image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:34.565647image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:36.859585image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:39.175294image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:41.501545image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:43.873402image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:45.872507image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:48.179123image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:34.845008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:37.175732image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:39.459502image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:41.771817image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:44.179575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:46.167710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:48.445406image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:35.168108image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:37.465908image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:39.732893image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:42.087022image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:44.457824image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-12-02T11:38:46.435961image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-12-02T11:39:13.393767image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
conditiondrivefuellatlongodometerpaintpredicted_pricepriceresidualtitletransmissiontypeyear
condition1.0000.0150.0460.0420.0290.2060.0370.1300.1990.0550.1070.0480.0540.196
drive0.0151.0000.1390.0970.0380.0900.1050.1110.1950.0660.0570.0730.5160.207
fuel0.0460.1391.0000.0190.0460.1210.0690.0940.1490.0730.0260.1750.2430.109
lat0.0420.0970.0191.000-0.1210.0640.012-0.069-0.0320.0900.0520.0280.025-0.103
long0.0290.0380.046-0.1211.000-0.0080.028-0.004-0.070-0.1340.0620.0290.0450.074
odometer0.2060.0900.1210.064-0.0081.0000.036-0.615-0.5050.0780.0790.0540.088-0.375
paint0.0370.1050.0690.0120.0280.0361.0000.0250.0480.0140.0230.0820.0980.105
predicted_price0.1300.1110.094-0.069-0.004-0.6150.0251.0000.820-0.1600.0450.0320.0580.577
price0.1990.1950.149-0.032-0.070-0.5050.0480.8201.0000.3230.0720.0290.0970.476
residual0.0550.0660.0730.090-0.1340.0780.014-0.1600.3231.0000.0240.0280.041-0.034
title0.1070.0570.0260.0520.0620.0790.0230.0450.0720.0241.0000.0340.0390.103
transmission0.0480.0730.1750.0280.0290.0540.0820.0320.0290.0280.0341.0000.1930.197
type0.0540.5160.2430.0250.0450.0880.0980.0580.0970.0410.0390.1931.0000.113
year0.1960.2070.109-0.1030.074-0.3750.1050.5770.476-0.0340.1030.1970.1131.000

Missing values

2024-12-02T11:38:50.143966image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-12-02T11:38:53.210672image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-12-02T11:39:00.400668image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

locationpriceodometeryearlonglatmaketitlepredicted_priceresidualtime_postedconditionurlstatemodelpaintfueldrivetypetransmission
0orlando12800.0260078.02015.0-81.36220028.393200freightlinerclean10037.2896272762.7103732024-11-28 11:14:15+00:00goodhttps://orlando.craigslist.org/cto/d/orlando-sprinter-van/7805995081.htmlFloridasprinter 2500whitedieselfwdvanautomatic
1atlanta5500.0116000.02016.0-83.86320034.296600buickcleanNaNNaN2024-11-27 10:04:06+00:00Nonehttps://atlanta.craigslist.org/nat/cto/d/gainesville-2016-buick-encore-sport/7805731876.htmlGeorgiaencoreNonegasNoneNoneautomatic
2siouxfalls16800.081100.02015.0-96.31030043.201400chevroletrebuilt16739.76065860.2393422024-11-27 07:15:17+00:00excellenthttps://siouxfalls.craigslist.org/cto/d/rock-valley-crew-cab-colorado/7805709785.htmlSouth Dakotacoloradoredgas4wdtruckautomatic
3lasvegas7900.089000.02008.0-116.93240033.796700pontiaccleanNaNNaN2024-11-25 14:54:13+00:00like newhttps://lasvegas.craigslist.org/cto/d/san-jacinto-absolutely-mint-condition/7805324202.htmlNevadasolsticeNonegasNoneNoneautomatic
4dallas5950.0138000.02012.0-96.83850032.960000audiclean8045.831956-2095.8319562024-11-24 13:36:04+00:00excellenthttps://dallas.craigslist.org/dal/cto/d/addison-2012-audi-q5-loaded-138k-nice/7804985720.htmlTexasq5browngasNoneSUVautomatic
5sfbay5900.0129000.02005.0-122.07750037.405600volkswagenclean2189.9876513710.0123492024-11-21 11:01:37+00:00goodhttps://sfbay.craigslist.org/sby/cto/d/mountain-view-2005-volkswagen-touareg-v6/7804146640.htmlCaliforniatouareggreengas4wdSUVautomatic
6portland4650.0204000.02003.0-123.50470045.082600nissanclean3604.5538451045.4461552024-11-19 09:30:51+00:00excellenthttps://portland.craigslist.org/yam/cto/d/willamina-03-nissan-pathfinder-le-4x4/7803524372.htmlOregonpathfindercustomgas4wdSUVautomatic
7miami900.0100000.02009.0-80.29260025.830100dodgecleanNaNNaN2024-11-07 12:36:31+00:00Nonehttps://miami.craigslist.org/mdc/cto/d/hialeah-2009-dodge-durango/7800091321.htmlFloridadurangoNonegasNoneSUVautomatic
8seattle5699.0123000.02009.0-122.44348548.790332fordcleanNaNNaN2024-11-05 14:30:48+00:00Nonehttps://seattle.craigslist.org/see/cto/d/bellingham-2009-ford-expedition-4x4/7799628737.htmlWashingtonexpeditionNonegasNoneSUVautomatic
9littlerock6200.0209085.02013.0-92.21065534.493909toyotaclean6647.499313-447.4993132024-11-05 11:51:33+00:00excellenthttps://littlerock.craigslist.org/cto/d/woodson-2013-prius-6200-obo/7799536266.htmlArkansasprius credhybridfwdhatchbackautomatic
locationpriceodometeryearlonglatmaketitlepredicted_priceresidualtime_postedconditionurlstatemodelpaintfueldrivetypetransmission
2290414sfbay6400.0200300.02004.0-121.152938.6709chevroletcleanNaNNaN2024-04-18 13:34:02+00:00Nonehttps://sfbay.craigslist.org/sfc/cto/d/represa-2004-chevrolet-silverado-v6/7738514683.htmlCaliforniasilverado 1500NonegasNoneNoneautomatic
2290415chicago3500.0198000.02006.0-87.838741.9279chevroletclean5304.407288-1804.4072882024-03-08 14:20:05+00:00goodhttps://chicago.craigslist.org/nch/cto/d/river-grove-2006-silverado-owner/7725231018.htmlIllinoissilverado 1500blackgasrwdtruckautomatic
2290416spacecoast4900.0200000.02007.0-80.403027.3215chevroletcleanNaNNaN2024-11-04 12:27:33+00:00Nonehttps://spacecoast.craigslist.org/cto/d/port-saint-lucie-2007-chevy-silverado/7799235343.htmlFloridasilverado 1500NonegasNonepickupautomatic
2290417vermont6000.0157000.02009.0-71.851044.4380chevroletclean11220.309546-5220.3095462024-09-06 11:00:06+00:00excellenthttps://vermont.craigslist.org/cto/d/concord-chevorlet-silverado-09/7782320378.htmlVermontsilverado 1500bluegasNonetruckautomatic
2290418sfbay15900.074000.02012.0-121.953037.3492chevroletclean19129.265004-3229.2650042024-07-27 11:26:05+00:00excellenthttps://sfbay.craigslist.org/sby/cto/d/santa-clara-2012-chevrolet-silverado/7770005116.htmlCaliforniasilverado 1500silvergasrwdtruckautomatic
2290419losangeles39000.032000.02020.0-118.329533.8708chevroletcleanNaNNaN2024-08-15 17:04:53+00:00Nonehttps://losangeles.craigslist.org/wst/cto/d/torrance-2020-chevrolet-silverado-1500/7775871256.htmlCaliforniasilverado 1500NonegasNonepickupautomatic
2290420sacramento4800.0220000.02003.0-120.851737.5239chevroletclean6532.142742-1732.1427422024-10-31 11:48:58+00:00excellenthttps://sacramento.craigslist.org/cto/d/turlock-2003-chevy-step-side/7798185582.htmlCaliforniasilverado 1500blackgasrwdtruckmanual
2290421losangeles17500.0170000.01992.0-118.325434.1005chevroletcleanNaNNaN2024-09-12 18:23:49+00:00Nonehttps://losangeles.craigslist.org/lac/cto/d/los-angeles-1992-chevy-shortbed-pickup/7784298349.htmlCaliforniasilverado 1500NonegasNonepickupautomatic
2290422poconos1500.0160000.02003.0-74.938041.2400chevroletcleanNaNNaN2024-04-09 08:00:31+00:00Nonehttps://poconos.craigslist.org/cto/d/dingmans-ferry-chevy-silverado/7735510884.htmlPennsylvaniasilverado 1500NonegasrwdNoneautomatic
2290423boone30000.046900.02019.0-81.157336.1359chevroletclean29318.781895681.2181052024-04-18 19:11:05+00:00excellenthttps://boone.craigslist.org/cto/d/wilkesboro-2019-silverado-ld-2wd/7738566141.htmlNorth Carolinasilverado 1500whitegasrwdtruckautomatic

Duplicate rows

Most frequently occurring

locationpriceodometeryearlonglatmaketitlepredicted_priceresidualtime_postedconditionurlstatemodelpaintfueldrivetypetransmission# duplicates
46175miami20900.088000.02018.0-80.20700025.851200fordclean10834.58060510065.4193952024-07-02 14:44:10+00:00like newhttps://miami.craigslist.org/mdc/cto/d/miami-2018-ford-mustang-convertible/7762499156.htmlFloridamustangwhitegasrwdconvertibleautomatic110
11953miami1500.0180000.02005.0-80.14235626.295185scionclean2463.666613-963.6666132024-06-18 12:29:42+00:00fairhttps://miami.craigslist.org/pbc/cto/d/deerfield-beach-2005-scion-xb-in-fair/7758116281.htmlFloridaxbgreygasfwdsedanautomatic95
20925miami4500.0146683.02012.0-80.05600026.618200kiacleanNaNNaN2024-07-02 13:40:53+00:00NaNhttps://miami.craigslist.org/pbc/cto/d/boca-raton-2012-kia-rio/7762474333.htmlFloridarioNaNgasNaNsedanautomatic95
33789miami8500.0154000.02006.0-80.08440026.346200gmcclean8067.815536432.1844642024-06-24 13:37:00+00:00goodhttps://miami.craigslist.org/pbc/cto/d/boca-raton-2006-gmc-sierra-1500-sle/7759974947.htmlFlorida1500greygasrwdpickupautomatic95
17416miami3500.0228000.02005.0-80.35880025.734300lexusclean3637.888707-137.8887072024-06-28 11:07:17+00:00goodhttps://miami.craigslist.org/mdc/cto/d/miami-2005-lexus-ls430/7761198368.htmlFloridalssilvergasfwdsedanautomatic90
26002miami5900.0101000.02011.0-80.40460025.596800fordrebuilt10574.741723-4674.7417232024-06-24 21:56:57+00:00like newhttps://miami.craigslist.org/mdc/cto/d/miami-2011-ford-mustang/7760134705.htmlFloridamustangblackgasrwdconvertiblemanual90
13982miami2500.0220000.02004.0-80.19500026.585000bmwcleanNaNNaN2024-06-08 08:51:23+00:00NaNhttps://miami.craigslist.org/pbc/cto/d/lake-worth-2004-bmw-325i-ice-cold-ac/7754977134.htmlFlorida2blackgasNaNsedanautomatic85
37872miami10500.0162000.02015.0-80.38090026.100300lexusclean8396.4197442103.5802562024-06-23 22:39:29+00:00goodhttps://miami.craigslist.org/brw/cto/d/fort-lauderdale-lexus-ct200h-2015/7759810242.htmlFloridactsilverhybridNaNhatchbackautomatic85
42685miami14900.044000.02019.0-80.41862925.598488gmcrebuilt20529.461749-5629.4617492024-06-26 12:51:10+00:00like newhttps://miami.craigslist.org/mdc/cto/d/miami-2019-gmc-acadia/7760605245.htmlFloridaacadiasilvergasfwdSUVautomatic85
20077miami4300.0129000.02010.0-80.12130026.263500volkswagenclean5220.294134-920.2941342024-06-24 21:33:50+00:00excellenthttps://miami.craigslist.org/brw/cto/d/pompano-beach-2010-vw-jetta/7760130431.htmlFloridajettablackgasNaNsedanautomatic80